Data mining is a big task at hand for companies small and large. Sifting through data to capitalize on groundbreaking analyses is under the priority list of every CFO. However, data mining isn’t the easiest process to understand and master. That’s why our experts at QuantumFBI are consistently called in to help explain the process to companies’ financial boards.

Understanding Data Mining

At Quantum FBI, we are experienced in a wide range of industries and through a long list of financial obstacles, so our team members have the jargon to effectively communicate the information your CFO needs to learn. Let’s walk through some of the basics.

What is data mining?

Data mining is a process that converts raw data into useful information. The software is used to mine huge amounts of data in search of patterns that communicate insights to business owners. These patterns found are unlikely to be observed or extracted without the help of a mining software, because the data collection is too big for an individual to sift through.

Data mining allows businesses to learn more about customers, to crafter more direct marketing strategies, to increase sales and decrease costs.

What is data warehousing?

Before a software system can mine through data and extract meaningful insights, there must be a solid warehouse in which all this data is contained. A data warehouse is a program or data base that houses all aggravated data and organizes it into useful segments. Warehouses can be set up to address specific needs, which is common when companies are after a particular consumer behavior insight.

What is data mining software?

Data mining software are the programs that go in and mine information inside the warehouse. Software programs analyze relationships and patterns to better under consumers and ultimately, to inform the decision-making processes of both consumer and company.

What are the data mining techniques?

There are five data mining techniques that your CFO can use to create optimal results for your company. The key techniques are examples of how data mining can be performed in different ways. There are Classification Analysis, Association rule learning, Anomaly, Regression Analysis. Let’s examine them in detail in the following section.

Classification analysis

Classification analysis segments data into assigned categories and then applies pre-established algorithms to mine those segments for particular extractions. Classification analysis can also help you determine how to best classify your data.

Association rule learning

This dependency modeling technique informs users of relationships within data, which is extremely beneficial is understanding consumer behavior. Association rule learning will identify small instances of recurring relationships until it identifies a pattern within the large set or subset of data. The relationships extracted provide a ton of insight on the way consumers behave. This technique is most common in retail, used for shopping basket analysis, product clustering, catalogue design and store layout.

Regression analysis

Regression is used to produce and forecast consumer behavior. Regression identifies which variables amongst relationships are dependent or independent, how those relationships are doing and whether or not either variable is changing. The analysis can then be used to apply marketing or product development efforts to the affected variables.

Which data mining technique you apply depends on which perspective you are analyzing your data through. If you are still wondering how to best analyze big data, we recommend hiring an experienced team.

Enlist our experts at QuantumFBI to walk your CFO through data mining techniques and big data analytics solutions. Both are critical to your business’s finances, and that’s why we’re here to help. Reach out to our team to learn more, to schedule a consolation or to get started with services right away.